Tomosynthesis is an imaging modality where typically projection radiographs from only a few angles within a relatively narrow angular range are acquired. From these projection radiographs, a 3D representation of the structure of the imaged object can be reconstructed. However, since only “incomplete” information exists (i.e., not densely spaced projections over the full angular range), the reconstruction problem is difficult to solve.
The main problems that need to be addressed by a reconstruction algorithm in order to yield satisfactory image quality are (i) efficient separation of overlying tissue, (ii) enhancement of the contrast, particularly of small structures, and (iii) minimization of artifacts. In some advanced reconstruction algorithms, some form of re-projection consistency constraint is utilized (directly or indirectly) to obtain high-quality reconstructions. These algorithms include, for example, additive algebraic reconstruction techniques (ART), matrix inversion tomosynthesis (MITS), Fourier based reconstruction, and volumetric order statistics-based reconstruction. However, these algorithms are generally computationally intensive and/or not very flexible to use. For example, strategies for artifact management may be hard to integrate in these known algorithms of the prior art.
A need exists for an improved, computationally efficient and flexible method for reconstructing a 3D image of an object from a plurality of projection radiographs.